January 25, 2020

3251 words 16 mins read

Paper Group ANR 1731

Anomaly Locality in Video Surveillance. Domain Partitioning Network. State Classification of Cooking Objects Using a VGG CNN. Deep Learning-Aided Tabu Search Detection for Large MIMO Systems. Optimal Estimation of Change in a Population of Parameters. Automatically Identifying Comparator Groups on Twitter for Digital Epidemiology of Pregnancy Outco …

Anomaly Locality in Video Surveillance


Title	Anomaly Locality in Video Surveillance
Authors	Federico Landi, Cees G. M. Snoek, Rita Cucchiara
Abstract	This paper strives for the detection of real-world anomalies such as burglaries and assaults in surveillance videos. Although anomalies are generally local, as they happen in a limited portion of the frame, none of the previous works on the subject has ever studied the contribution of locality. In this work, we explore the impact of considering spatiotemporal tubes instead of whole-frame video segments. For this purpose, we enrich existing surveillance videos with spatial and temporal annotations: it is the first dataset for anomaly detection with bounding box supervision in both its train and test set. Our experiments show that a network trained with spatiotemporal tubes performs better than its analogous model trained with whole-frame videos. In addition, we discover that the locality is robust to different kinds of errors in the tube extraction phase at test time. Finally, we demonstrate that our network can provide spatiotemporal proposals for unseen surveillance videos leveraging only video-level labels. By doing, we enlarge our spatiotemporal anomaly dataset without the need for further human labeling.
Tasks	Anomaly Detection
Published	2019-01-29
URL	http://arxiv.org/abs/1901.10364v1
PDF	http://arxiv.org/pdf/1901.10364v1.pdf
PWC	https://paperswithcode.com/paper/anomaly-locality-in-video-surveillance
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Domain Partitioning Network


Title	Domain Partitioning Network
Authors	Botos Csaba, Adnane Boukhayma, Viveka Kulharia, András Horváth, Philip H. S. Torr
Abstract	Standard adversarial training involves two agents, namely a generator and a discriminator, playing a mini-max game. However, even if the players converge to an equilibrium, the generator may only recover a part of the target data distribution, in a situation commonly referred to as mode collapse. In this work, we present the Domain Partitioning Network (DoPaNet), a new approach to deal with mode collapse in generative adversarial learning. We employ multiple discriminators, each encouraging the generator to cover a different part of the target distribution. To ensure these parts do not overlap and collapse into the same mode, we add a classifier as a third agent in the game. The classifier decides which discriminator the generator is trained against for each sample. Through experiments on toy examples and real images, we show the merits of DoPaNet in covering the real distribution and its superiority with respect to the competing methods. Besides, we also show that we can control the modes from which samples are generated using DoPaNet.
Tasks
Published	2019-02-21
URL	http://arxiv.org/abs/1902.08134v1
PDF	http://arxiv.org/pdf/1902.08134v1.pdf
PWC	https://paperswithcode.com/paper/domain-partitioning-network
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State Classification of Cooking Objects Using a VGG CNN


Title	State Classification of Cooking Objects Using a VGG CNN
Authors	Kyle Mott
Abstract	In machine learning, it is very important for a robot to know the state of an object and recognize particular desired states. This is an image classification problem that can be solved using a convolutional neural network. In this paper, we will discuss the use of a VGG convolutional neural network to recognize those states of cooking objects. We will discuss the uses of activation functions, optimizers, data augmentation, layer additions, and other different versions of architectures. The results of this paper will be used to identify alternatives to the VGG convolutional neural network to improve accuracy.
Tasks	Data Augmentation, Image Classification
Published	2019-04-21
URL	http://arxiv.org/abs/1904.12613v1
PDF	http://arxiv.org/pdf/1904.12613v1.pdf
PWC	https://paperswithcode.com/paper/190412613
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Deep Learning-Aided Tabu Search Detection for Large MIMO Systems


Title	Deep Learning-Aided Tabu Search Detection for Large MIMO Systems
Authors	NhanThanh Nguyen, Kyungchun Lee
Abstract	In this study, we consider the application of deep learning (DL) to tabu search (TS) detection in large multiple-input multiple-output (MIMO) systems. First, we propose a deep neural network architecture for symbol detection, termed the fast-convergence sparsely connected detection network (FS-Net), which is obtained by optimizing the prior detection networks called DetNet and ScNet. Then, we propose the DL-aided TS algorithm, in which the initial solution is approximated by the proposed FS-Net. Furthermore, in this algorithm, an adaptive early termination algorithm and a modified searching process are performed based on the predicted approximation error, which is determined from the FS-Net-based initial solution, so that the optimal solution can be reached earlier. The simulation results show that the proposed algorithm achieves approximately 90% complexity reduction for a $32 \times 32$ MIMO system with QPSK with respect to the existing TS algorithms, while maintaining almost the same performance.
Tasks
Published	2019-09-04
URL	https://arxiv.org/abs/1909.01683v1
PDF	https://arxiv.org/pdf/1909.01683v1.pdf
PWC	https://paperswithcode.com/paper/deep-learning-aided-tabu-search-detection-for
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Optimal Estimation of Change in a Population of Parameters


Title	Optimal Estimation of Change in a Population of Parameters
Authors	Ramya Korlakai Vinayak, Weihao Kong, Sham M. Kakade
Abstract	Paired estimation of change in parameters of interest over a population plays a central role in several application domains including those in the social sciences, epidemiology, medicine and biology. In these domains, the size of the population under study is often very large, however, the number of observations available per individual in the population is very small (\emph{sparse observations}) which makes the problem challenging. Consider the setting with $N$ independent individuals, each with unknown parameters $(p_i, q_i)$ drawn from some unknown distribution on $[0, 1]^2$. We observe $X_i \sim \text{Bin}(t, p_i)$ before an event and $Y_i \sim \text{Bin}(t, q_i)$ after the event. Provided these paired observations, ${(X_i, Y_i) }_{i=1}^N$, our goal is to accurately estimate the \emph{distribution of the change in parameters}, $\delta_i := q_i - p_i$, over the population and properties of interest like the \emph{$\ell_1$-magnitude of the change} with sparse observations ($t\ll N$). We provide \emph{information theoretic lower bounds} on the error in estimating the distribution of change and the $\ell_1$-magnitude of change. Furthermore, we show that the following two step procedure achieves the optimal error bounds: first, estimate the full joint distribution of the paired parameters using the maximum likelihood estimator (MLE) and then estimate the distribution of change and the $\ell_1$-magnitude of change using the joint MLE. Notably, and perhaps surprisingly, these error bounds are of the same order as the minimax optimal error bounds for learning the \emph{full} joint distribution itself (in Wasserstein-1 distance); in other words, estimating the magnitude of the change of parameters over the population is, in a minimax sense, as difficult as estimating the full joint distribution itself.
Tasks	Epidemiology
Published	2019-11-28
URL	https://arxiv.org/abs/1911.12568v1
PDF	https://arxiv.org/pdf/1911.12568v1.pdf
PWC	https://paperswithcode.com/paper/optimal-estimation-of-change-in-a-population
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Automatically Identifying Comparator Groups on Twitter for Digital Epidemiology of Pregnancy Outcomes


Title	Automatically Identifying Comparator Groups on Twitter for Digital Epidemiology of Pregnancy Outcomes
Authors	Ari Z. Klein, Abeselom Gebreyesus, Graciela Gonzalez-Hernandez
Abstract	Despite the prevalence of adverse pregnancy outcomes such as miscarriage, stillbirth, birth defects, and preterm birth, their causes are largely unknown. We seek to advance the use of social media for observational studies of pregnancy outcomes by developing a natural language processing pipeline for automatically identifying users from which to select comparator groups on Twitter. We annotated 2361 tweets by users who have announced their pregnancy on Twitter, which were used to train and evaluate supervised machine learning algorithms as a basis for automatically detecting women who have reported that their pregnancy had reached term and their baby was born at a normal weight. Upon further processing the tweet-level predictions of a majority voting-based ensemble classifier, the pipeline achieved a user-level F1-score of 0.933, with a precision of 0.947 and a recall of 0.920. Our pipeline will be deployed to identify large comparator groups for studying pregnancy outcomes on Twitter.
Tasks	Epidemiology
Published	2019-08-16
URL	https://arxiv.org/abs/1908.06015v1
PDF	https://arxiv.org/pdf/1908.06015v1.pdf
PWC	https://paperswithcode.com/paper/automatically-identifying-comparator-groups
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Toponym Identification in Epidemiology Articles - A Deep Learning Approach


Title	Toponym Identification in Epidemiology Articles - A Deep Learning Approach
Authors	MohammadReza Davari, Leila Kosseim, Tien D. Bui
Abstract	When analyzing the spread of viruses, epidemiologists often need to identify the location of infected hosts. This information can be found in public databases, such as GenBank, however, information provided in these databases are usually limited to the country or state level. More fine-grained localization information requires phylogeographers to manually read relevant scientific articles. In this work we propose an approach to automate the process of place name identification from medical (epidemiology) articles. The focus of this paper is to propose a deep learning based model for toponym detection and experiment with the use of external linguistic features and domain specific information. The model was evaluated using a collection of 105 epidemiology articles from PubMed Central provided by the recent SemEval task 12. Our best detection model achieves an F1 score of $80.13%$, a significant improvement compared to the state of the art of $69.84%$. These results underline the importance of domain specific embedding as well as specific linguistic features in toponym detection in medical journals.
Tasks	Epidemiology
Published	2019-04-24
URL	http://arxiv.org/abs/1904.11018v2
PDF	http://arxiv.org/pdf/1904.11018v2.pdf
PWC	https://paperswithcode.com/paper/toponym-identification-in-epidemiology
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DENet: A Universal Network for Counting Crowd with Varying Densities and Scales


Title	DENet: A Universal Network for Counting Crowd with Varying Densities and Scales
Authors	Lei Liu, Jie Jiang, Wenjing Jia, Saeed Amirgholipour, Michelle Zeibots, Xiangjian He
Abstract	Counting people or objects with significantly varying scales and densities has attracted much interest from the research community and yet it remains an open problem. In this paper, we propose a simple but an efficient and effective network, named DENet, which is composed of two components, i.e., a detection network (DNet) and an encoder-decoder estimation network (ENet). We first run DNet on an input image to detect and count individuals who can be segmented clearly. Then, ENet is utilized to estimate the density maps of the remaining areas, where the numbers of individuals cannot be detected. We propose a modified Xception as an encoder for feature extraction and a combination of dilated convolution and transposed convolution as a decoder. In the ShanghaiTech Part A, UCF and WorldExpo’10 datasets, our DENet achieves lower Mean Absolute Error (MAE) than those of the state-of-the-art methods.
Tasks
Published	2019-04-17
URL	http://arxiv.org/abs/1904.08056v1
PDF	http://arxiv.org/pdf/1904.08056v1.pdf
PWC	https://paperswithcode.com/paper/denet-a-universal-network-for-counting-crowd
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Singing voice synthesis based on convolutional neural networks


Title	Singing voice synthesis based on convolutional neural networks
Authors	Kazuhiro Nakamura, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, Keiichi Tokuda
Abstract	The present paper describes a singing voice synthesis based on convolutional neural networks (CNNs). Singing voice synthesis systems based on deep neural networks (DNNs) are currently being proposed and are improving the naturalness of synthesized singing voices. In these systems, the relationship between musical score feature sequences and acoustic feature sequences extracted from singing voices is modeled by DNNs. Then, an acoustic feature sequence of an arbitrary musical score is output in units of frames by the trained DNNs, and a natural trajectory of a singing voice is obtained by using a parameter generation algorithm. As singing voices contain rich expression, a powerful technique to model them accurately is required. In the proposed technique, long-term dependencies of singing voices are modeled by CNNs. An acoustic feature sequence is generated in units of segments that consist of long-term frames, and a natural trajectory is obtained without the parameter generation algorithm. Experimental results in a subjective listening test show that the proposed architecture can synthesize natural sounding singing voices.
Tasks
Published	2019-04-15
URL	https://arxiv.org/abs/1904.06868v2
PDF	https://arxiv.org/pdf/1904.06868v2.pdf
PWC	https://paperswithcode.com/paper/singing-voice-synthesis-based-on
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Are All Layers Created Equal?


Title	Are All Layers Created Equal?
Authors	Chiyuan Zhang, Samy Bengio, Yoram Singer
Abstract	Understanding deep neural networks has been a major research objective in recent years with notable theoretical progress. A focal point of those studies stems from the success of excessively large networks which defy the classical wisdom of uniform convergence and learnability. We study empirically the layer-wise functional structure of overparameterized deep models. We provide evidence for the heterogeneous characteristic of layers. To do so, we introduce the notion of robustness to post-training re-initialization and re-randomization. We show that the layers can be categorized as either `ambient'' or` critical’'. Resetting the ambient layers to their initial values has no negative consequence, and in many cases they barely change throughout training. On the contrary, resetting the critical layers completely destroys the predictor and the performance drops to chanceh. Our study provides further evidence that mere parameter counting or norm accounting is too coarse in studying generalization of deep models, and flatness or robustness analysis of the models needs to respect the network architectures.
Tasks
Published	2019-02-06
URL	https://arxiv.org/abs/1902.01996v3
PDF	https://arxiv.org/pdf/1902.01996v3.pdf
PWC	https://paperswithcode.com/paper/are-all-layers-created-equal
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Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition


Title	Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition
Authors	Chengjin Xu, Mojtaba Nayyeri, Fouad Alkhoury, Jens Lehmann, Hamed Shariat Yazdi
Abstract	Knowledge Graph (KG) embedding has attracted more attention in recent years. Most of KG embedding models learn from time-unaware triples. However, the inclusion of temporal information beside triples would further improve the performance of a KGE model. In this regard, we propose ATiSE, a temporal KG embedding model which incorporates time information into entity/relation representations by using Additive Time Series decomposition. Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions. The mean of each entity/relation embedding at a time step shows the current expected position, whereas its covariance (which is temporally stationary) represents its temporal uncertainty. Experimental results show that ATiSE not only achieves the state-of-the-art on link prediction over temporal KGs, but also can predict the occurrence time of facts with missing time annotations, as well as the existence of future events. To the best of our knowledge, no other model is capable to perform all these tasks.
Tasks	Graph Embedding, Knowledge Graph Embedding, Link Prediction, Time Series
Published	2019-11-18
URL	https://arxiv.org/abs/1911.07893v1
PDF	https://arxiv.org/pdf/1911.07893v1.pdf
PWC	https://paperswithcode.com/paper/temporal-knowledge-graph-embedding-model
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Semiparametric Methods for Exposure Misclassification in Propensity Score-Based Time-to-Event Data Analysis


Title	Semiparametric Methods for Exposure Misclassification in Propensity Score-Based Time-to-Event Data Analysis
Authors	Yingrui Yang, Molin Wang
Abstract	In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situations involving exposure measured with moderate to substantial error, identifying the exposure effect using propensity score in Cox models remains a challenging yet unresolved problem. In this paper, we propose an estimating equation method to correct for the exposure misclassification-caused bias in the estimation of exposure-outcome associations. We also discuss the asymptotic properties and derive the asymptotic variances of the proposed estimators. We conduct a simulation study to evaluate the performance of the proposed estimators in various settings. As an illustration, we apply our method to correct for the misclassification-caused bias in estimating the association of PM2.5 level with lung cancer mortality using a nationwide prospective cohort, the Nurses’ Health Study (NHS). The proposed methodology can be applied using our user-friendly R function published online.
Tasks	Epidemiology
Published	2019-03-19
URL	https://arxiv.org/abs/1903.07782v2
PDF	https://arxiv.org/pdf/1903.07782v2.pdf
PWC	https://paperswithcode.com/paper/semiparametric-methods-for-exposure
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Urban flows prediction from spatial-temporal data using machine learning: A survey


Title	Urban flows prediction from spatial-temporal data using machine learning: A survey
Authors	Peng Xie, Tianrui Li, Jia Liu, Shengdong Du, Xin Yang, Junbo Zhang
Abstract	Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different domains, e.g. mobile phone data, taxi trajectories data, metro/bus swiping data, bike-sharing data and so on. To summarize these methodologies of urban flows prediction, in this paper, we first introduce four main factors affecting urban flows. Second, in order to further analysis urban flows, a preparation process of multi-sources spatial-temporal data related with urban flows is partitioned into three groups. Third, we choose the spatial-temporal dynamic data as a case study for the urban flows prediction task. Fourth, we analyze and compare some well-known and state-of-the-art flows prediction methods in detail, classifying them into five categories: statistics-based, traditional machine learning-based, deep learning-based, reinforcement learning-based and transfer learning-based methods. Finally, we give open challenges of urban flows prediction and an outlook in the future of this field. This paper will facilitate researchers find suitable methods and open datasets for addressing urban spatial-temporal flows forecast problems.
Tasks	Transfer Learning
Published	2019-08-26
URL	https://arxiv.org/abs/1908.10218v1
PDF	https://arxiv.org/pdf/1908.10218v1.pdf
PWC	https://paperswithcode.com/paper/urban-flows-prediction-from-spatial-temporal
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Tensor Decomposition with Relational Constraints for Predicting Multiple Types of MicroRNA-disease Associations


Title	Tensor Decomposition with Relational Constraints for Predicting Multiple Types of MicroRNA-disease Associations
Authors	Feng Huang, Xiang Yue, Zhankun Xiong, Zhouxin Yu, Wen Zhang
Abstract	MicroRNAs (miRNAs) play crucial roles in multifarious biological processes associated with human diseases. Identifying potential miRNA-disease associations contributes to understanding the molecular mechanisms of miRNA-related diseases. Most of the existing computational methods mainly focus on predicting whether a miRNA-disease association exists or not. However, the roles of miRNAs in diseases are prominently diverged, for instance, Genetic variants of microRNA (mir-15) may affect expression level of miRNAs leading to B cell chronic lymphocytic leukemia, while circulating miRNAs (including mir-1246, mir-1307-3p, etc.) have potentials to detecting breast cancer in the early stage. In this paper, we aim to predict multi-type miRNA-disease associations instead of taking them as binary. To this end, we innovatively represent miRNA-disease-type triplets as a tensor and introduce Tensor Decomposition methods to solve the prediction task. Experimental results on two widely-adopted miRNA-disease datasets: HMDD v2.0 and HMDD v3.2 show that tensor decomposition methods improve a recent baseline in a large scale (up to 38% in top-1 F1). We further propose a novel method, Tensor Decomposition with Relational Constraints (TDRC), which incorporates biological features as relational constraints to further the existing tensor decomposition methods. Compared with two existing tensor decomposition methods, TDRC can produce better performance while being more efficient.
Tasks	Knowledge Graphs, Link Prediction
Published	2019-11-13
URL	https://arxiv.org/abs/1911.05584v2
PDF	https://arxiv.org/pdf/1911.05584v2.pdf
PWC	https://paperswithcode.com/paper/predicting-microrna-disease-associations-from
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SegTHOR: Segmentation of Thoracic Organs at Risk in CT images


Title	SegTHOR: Segmentation of Thoracic Organs at Risk in CT images
Authors	Z. Lambert, C. Petitjean, B. Dubray, S. Ruan
Abstract	In the era of open science, public datasets, along with common experimental protocol, help in the process of designing and validating data science algorithms; they also contribute to ease reproductibility and fair comparison between methods. Many datasets for image segmentation are available, each presenting its own challenges; however just a very few exist for radiotherapy planning. This paper is the presentation of a new dataset dedicated to the segmentation of organs at risk (OARs) in the thorax, i.e. the organs surrounding the tumour that must be preserved from irradiations during radiotherapy. This dataset is called SegTHOR (Segmentation of THoracic Organs at Risk). In this dataset, the OARs are the heart, the trachea, the aorta and the esophagus, which have varying spatial and appearance characteristics. The dataset includes 60 3D CT scans, divided into a training set of 40 and a test set of 20 patients, where the OARs have been contoured manually by an experienced radiotherapist. Along with the dataset, we present some baseline results, obtained using both the original, state-of-the-art architecture U-Net and a simplified version. We investigate different configurations of this baseline architecture that will serve as comparison for future studies on the SegTHOR dataset. Preliminary results show that room for improvement is left, especially for smallest organs.
Tasks	Semantic Segmentation
Published	2019-12-12
URL	https://arxiv.org/abs/1912.05950v1
PDF	https://arxiv.org/pdf/1912.05950v1.pdf
PWC	https://paperswithcode.com/paper/segthor-segmentation-of-thoracic-organs-at
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